Unsupervised deep learning in computer vision typically uses standard convolutional network (CNN) models [1]}, such as ResNets [2]}. Transferring these models is relatively straightforward because CNNs are in widespread use in most downstream tasks, and thus benchmarking protocols are easy to define and baselines are plentiful ( [3]}). In other words, unsupervised learning with CNNs produces a plug-and-play parameter initialization.
